Ever since the first manufactured planes took flight, the aviation industry has always been at the heart of modern science and engineering. It is but natural that they are still using the latest technologies, data, and observations to improve the flying experience in today's modern age.
Today, from the moment you start thinking about booking a flight to the moment you or your goods step out of the flight, there are innumerable ways that data and machine learning continue to transform the entire aviation industry.
According to a report by Markets and Markets, the aviation analytics market is prepared to grow from USD 1.7 billion in 2020 to USD 3.0 billion by 2025. Let's take a deeper look at some areas in which data science and machine learning have brought a massive transformation.
Flight safety
From day one to even now, commercial flights have been under intense scrutiny for the safety and security of goods and passengers. Airlines have to ensure that their flights surpass all mandatory safety requirements while maintaining and improving the quality of their services, providing customers' comfort while keeping the flight's costs within tight budget constraints. And now, after the covid pandemic, ensuring sanitization standards and screening for potential disease-bearing travelers added another level of complication into the mix.
According to PwC, unplanned security maintenance causes up to 30% of total delay time in transportation, which predictive analytics can quickly reduce to a great extent. From manufacturing plants and assembly lines to airport review sites before and after each flight, technology developed with state of the art computer vision models also accurately measures the dimensions, weight, size, and count of the goods before optimizing the goods' location for safety at every touchpoint.
The ability to closely monitor passengers and goods transported without putting other humans at risk, identifying vulnerabilities, and even enabling safeguards against them has been possible only because of the advances in data science.
Revenue Management & Pricing Optimisation
Have you ever noticed the flight prices suddenly rise as the flight gets booked more and more? The factors behind this are not as simple as competing prices or risk, but incorporate an increase in operational workload, the cost of fuel increasing with the weight that a flight has to carry, etc.
Furthermore, airlines aren’t really selling seats from A to B anymore. They sell seat selection, extra leg room, pre-ordered meals on board, merchandise, extra bags on board, extra check-in bags, priority boarding etc. In fact, low-are airlines have mastered the ancillary product part. Wizz Air, a low-cost European airline, makes around 40% of its revenue from ancillary products alone.
Obviously, airlines have come far since the 70s when revenue management started to dominate the industry. Now, there’s even one-on-one pricing models being implemented. For example, what if John and Jane are both flying from Dublin to Madrid. The airline knows that John has never purchased a seat in the past, never buys anything on board, never checks a bag and literally only pays for the seat. Jane, on the other hand, always checks in an extra bag, purchases priority boarding, and pays extra to sit at the front by the aisle. Obviously, the airline makes more money from Jane than John. Therefore, the airline should be willing to offer Jane a ticket at a lower price than John.
Data science and predictive analytics using machine learning techniques, is taking pricing optimization to the next level. The technology used today was not even available 5 years ago, making this truly a revolutionising period where everything is new for most organisations.
Airlines today are on a strict budget. Modern revenue-management systems help airlines schedule airplanes on different routes, select passengers on connecting trips, and ensure that most planes depart at the max possible capacity while maintaining security standards and incorporating customer preferences.
These efficiencies generally flow through to passengers, who can then give less for each ticket and service. These systems try to integrate information from all kinds of data and models available such that suitable potential passengers get to pay the right price for a flight and that the airlines can also successfully sustain themselves.
Customer Experience
When a carrier is in the air, it is natural for both passengers and goods owners to get anxious about their flights and track them. These days, because of the transformation brought about by data and data science, it is common for customers to know their flight status and expected flight duration in real-time.
While commercial flights use the latest technological developments to make real-time information on cargo available, premium passenger airlines periodically make their current location and their speed, altitude, etc., available even during the flight. To improve the passengers' experience, several airlines have started using sentiment analysis techniques to provide amenities like customized in-flight meals, movies, and other media and entertainment tools during the in-flight journey using feedback analysis.
According to a recent Mckinsey report, systems that improved the targeting and timeliness of offers across specific customer segments increased booking conversion rates by up to one percentage point and reduced marketing costs by 5 to 10 percent.
Conclusion
Commercial and passenger flights across the globe have undergone a massive transformation in recent years because of the increasing importance given to data and its proper utilization. During the pandemic, the lack of passengers had brought down passenger airline usage to a minimum, and commercial freight flights were the lifeline for the aviation industry. However, according to McKinsey, the improvement in digital infrastructure brought about by data and its usage has much longer impacts. It will continue to help the industry for years to come.
